Language Model Security Database

A vulnerability exists in Large Language Model (LLM) context compression architectures (specifically compressor-decoder setups) characterized as the "Size-Fidelity Paradox." When scaling up the parameter count of the compressor model (e.g., beyond 4B parameters in Qwen-3 and LLaMA-3.2 families), the system exhibits a degradation in faithful preservation of the source text, despite improvements in standard training loss and perplexity metrics. This degradation manifests through two primary…

Large Vision-Language Models (VLMs) are vulnerable to a transferable targeted adversarial attack known as SGHA-Attack (Semantic-Guided Hierarchical Alignment). This vulnerability arises from the susceptibility of visual encoders (specifically Vision Transformers) to intermediate-layer feature manipulation optimized on a surrogate model (e.g., CLIP). An attacker can craft adversarial images by injecting imperceptible perturbations that enforce semantic consistency with a target text prompt…

Point-based 3D Vision-Language Models (VLMs), specifically PointLLM and GPT4Point, are vulnerable to white-box, gradient-based adversarial attacks. The vulnerability exists in the model's processing of 3D point cloud data, where an attacker can optimize imperceptible geometric perturbations ($\delta$) on the input point cloud ($x$) to manipulate the model's textual output. The paper identifies two specific attack vectors: 1. Vision Attack: Directly perturbs the high-dimensional visual token…

A vulnerability exists in trimodal audio-video-language models where an attacker can systematically degrade multimodal reasoning through untargeted, audio-only adversarial perturbations. By optimizing a shared perturbation $\delta$ applied to the audio channel, an attacker can manipulate internal representations—specifically targeting audio encoder embeddings and cross-modal attention mechanisms—without modifying visual or textual inputs. The attack exploits the model's reliance on the audio…

Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…